The wireless sensor network is used to cooperatively sense,collect,and process information of the perceived object in the network coverage area and send it to the observer.Due to its low cost,multi-function and multi-gate technology,it is regarded as one of the most influential technologies in the 21st century.The Applications of WSN include video surveillance,air traffic control and robotics.Node positioning is an important supporting technology for WSN.Its accuracy is an important criterion for measuring the performance of WSN.RSSI is widely used because it uses a wireless communication chip,does not require additional equipment,is easy to use and has high precision.However,there are errors in the ranging.It is a research hotspot to reduce the error by transforming the positioning problem of the wireless sensor into the optimization problem of finding the minimum value of the ranging error.In this paper,the improved bat algorithm is introduced into the optimization problem of node position solution to compensate for the influence of ranging error on the accuracy of positioning results and improve the accuracy of positioning.The bat algorithm is a random search algorithm.It has the characteristics of parallelism,distribution and fast convergence.So it has been widely used in engineering design and subject areas.Because of its slow convergence in the later period,low convergence precision and easy to fall into local minimum values,the paper improves from the global optimization and local search based on the BA as to converge to a better solution faster.The main research work of this thesis is as follows:(1)A wireless sensor network node localization method based on a hybrid BA based on memetic framework is proposed.The improved method relies on the framework of the meme algorithm.The BA is used as the global search algorithm,and selection operator and the perturbation operator are proposed to extend the global optimization ability.At the same time,the local search strategy based on random adjustment is used as the local search method to prevent the algorithm from falling into local optimum.The selection operator is to make up for the excellent information lost in the algorithm that is inferior to the parent but better than the worst in the current population.The perturbation operator uses the adaptive t-distribution to perturb the optimal solution,helping it to jump out of the local optimal value and converge to a better solution.The local search strategy based on random adjustment searches the neighborhood around the feasible solution,and determines whether to adopt the solution by comparing the fitness value,so as to avoid falling into local optimum.Through the verification of the standard test function,the proposed algorithm has a greater improvement in convergence speed and convergence accuracy than BA and CBA.Next,the improved method is applied to the WSN node positioning.It can be seen from the experimental simulation results that the accuracy is about 0.5m higher than that of the CBA,and it has a greater advantage in positioning.(2)A WSN node localization method based on interspecific dual-system cooperative bat optimization algorithm is proposed.For the single population,due to the characteristics of the algorithm itself and the search area,it is difficult to further explore and study the current optimal value without neglecting the problems of the areas that have not been developed in the region,so the concept of the dual system of species is proposed.They are the detection system and the development system,and the two evolve and collaborate through information exchange.The dynamic change operator is designed and applied to realize the real-time balance between global and local optimization in this algorithm.The position update operator is proposed to reduce the impact of the randomness of the development system and improve the development efficiency of the local area.The pseudo-mutation operator is designed to maintain the diversity of the detection system and improve the efficiency of global search.Through the verification of the standard test function,the proposed algorithm can avoid the local optimization while fast convergence,and it is very effective for solving the complex optimization problem of multi-local extremum.Next,the improved method is applied to the WSN node positioning.It can be seen from the experimental simulation results that the accuracy is about 0.7m higher than that of the CBA,and the positioning accuracy is higher.(3)A WSN node localization method based on cellular bat algorithm is proposed.The improved algorithm integrates the idea of cellular automata into the bat algorithm from the perspective that only neighboring individuals can interact.So that it only can communicate with the neighbors determined by the neighbor function,which helps to maintain the diversity and exploration capabilities.And improved restricted competition selection niche technique is adopted.It ensures that individual cell learns from the best individuals in the neighborhood and transmits effective information to each other,so as to improve the diversity of the population and avoid the situation that the algorithm falls into local optimum.At the same time,the disaster mechanism is added,that is,the fixed area of the cell is perturbed at a certain frequency,so that the algorithm can jump out of the local extremum and maintain the ability of continuous evolution.Next,the improved method is applied to the WSN node positioning.It can be seen from the experimental simulation results that the positioning accuracy is improved.And in the actual experimental experiment,the average positioning error of the improved algorithm in the test environment is less than 0.4m,what's more,better positioning effect is obtained compared with the improved PSO algorithm. |